Means and method for predicting diabetes

ABSTRACT

The present invention relates to a method for diagnosing a predisposition for diabetes, comprising determining a metabolite in a test sample of a subject suspected to have a predisposition for diabetes and comparing the metabolite to a reference to diagnose the, predisposition for diabetes. Moreover, the present invention encompasses a collection of metabolites, a data collection comprising characteristic values of metabolites and a storage medium comprising the data collection. The present invention also relates to a system comprising methods or devices for comparing characteristic values of metabolites of a sample operatively linked to a data storage medium. Additionally, diagnostic methods or devices comprising a metabolite and its use for manufacture of the diagnostic method or device for diagnosing a predisposition for diabetes are provided, along with a method for identifying diabetes-related metabolites.

RELATED APPLICATIONS

This application is a national stage application (under 35 U.S.C. 371)of PCT/EP2007/052692, filed Mar. 21, 2007, which claims benefit ofEuropean application 06111705.7 filed Mar. 24, 2006 and Europeanapplication 06120273.5, filed Sep. 7, 2006.

The present invention relates to a method for diagnosing apredisposition for diabetes comprising determining at least onemetabolite in a test sample of a subject suspected to have apredisposition for diabetes and comparing said at least one metaboliteto a reference, whereby a predisposition for diabetes is to bediagnosed. Moreover, the present invention encompasses a collection ofmetabolites, a data collection comprising characteristic values ofmetabolites and a storage medium comprising said data collection.Furthermore, the present invention also relates to a system comprisingmeans for comparing characteristic values of metabolites of a sampleoperatively linked to a data storage medium. Further encompassed by thepresent invention are diagnostic means comprising at least onemetabolite and the use of said at least one metabolite for themanufacture of diagnostic means for diagnosing a predisposition fordiabetes. Finally, the present invention pertains to a method foridentifying diabetes-related metabolites.

The prevalence of diabetes mellitus has reached about 6% in theindustrialised world and will increase up to 366 million affected peoplein 2030 worldwide. The most frequently reason (type), (about 90%) fordiabetes in the world is accounted for type 2 diabetes, which has amultifactorial pathogenesis. The pathological sequence for type 2diabetes entails many elements. It is believed to be mandatory to have agenetic predisposition that is currently poorly understood. Whether thediabetes phenotype then occurs is influenced by many environmentalfactors that share an ability to stress the glucose homeostasis system,either by causing or worsening insulin resistance or impairing insulinsecretion. Of course many hormones are taking part in the regulation ofglucose metabolism, but the key hormone is insulin. Normoglycaemia ismaintained by the balanced interplay between insulin action and insulinsecretion. Insulin is produced by the pancreatic β-cell which is able toregulate very fast to different glucose demands. The main reason fortype 2 diabetes is an increasing insulin resistance. Therefore, insulinaction normally decrease but initially the system is able to compensatethis by an increasing β-cell function. At this time perhaps only animpaired fasting glucose or an impaired glucose tolerance in the OGTTcould be measured. But over time the β-cell will be overstressed byincreasing insulin resistance and glucose toxicity and a type 2 diabetescould be diagnosed.

Apart from direct medical problems by high or low blood sugar the mainmedical and socioeconomic burden of the disease is caused by theassociated complications. The devastating complications of diabetesmellitus are mostly macrovascular and microvascular diseases likechronic renal failure, retinopathy, periphery and autonomic neuropathyor myocardial infarction. Therefore, cardiovascular morbidity inpatients with type 2 diabetes is two to four times greater than that ofnon-diabetic people (Stumvoll et al., Type 2 diabetes: principles ofpathogenesis and therapy, Lancet 2005).

In light of this mechanism, therapy of diabetes is currently based onmonitoring the blood sugar levels and reducing an elevated level ofblood sugar into a normal level by administration of exogenous insulin.To this end, exogenous insulin is injected into the blood.Alternatively, glucose levels in the blood may be regulated bynutritional diets and the exclusion of life-style risk factors, such assmoking, lack of exercise, high cholesterol levels, and an unstable bodyweight.

The Expert Committee of the ADA (American Diabetes Association)recognized an intermediate group of subjects whose glucose levels,although not meeting criteria for diabetes, are nevertheless too high tobe considered normal. This group is defined as having fasting plasmaglucose (FPG) levels >100 mg/dl (5.6 mmol/l) but <126 mg/dl (7.0 mmol/l)or 2-h values in the oral glucose tolerance test (OGTT) of >140 mg/dl(7.8 mmol/l) but <200 mg/di (11.1 mmol/l). Thus, the categories of FPGvalues are as follows:

-   -   FPG<100 mg/dl (5.6 mmol/l)=normal fasting glucose;    -   FPG 100-125 mg/dl (5.6-6.9 mmol/l)=IFG (impaired fasting        glucose);    -   FPG>126 mg/dl (7.0 mmol/l)=provisional diagnosis of diabetes        (the diagnosis must be confirmed, as described below).

The corresponding categories when the OGTT is used are the following:

-   -   2-h postload glucose <140 mg/dl (7.8 mmol/l)=normal glucose        tolerance    -   2-h postload glucose 140-199 mg/dl (7.8-11.1 mmol/l)=IGT        (impaired glucose tolerance)    -   2-h postload glucose >200 mg/dl (11.1 mmol/l)=provisional        diagnosis of diabetes (the diagnosis must be confirmed, as        described below).        Diagnosis of Diabetes Mellitus Type 2:        1. Symptoms of diabetes plus casual plasma glucose        concentration >200 mg/dl (11.1 mmol/l). Casual is defined as any        time of day without regard to time since last meal. The classic        symptoms of diabetes include polyuria, polydipsia, and        unexplained weight loss. Alternatively: 2. FPG>126 mg/dl (7.0        mmol/l). Fasting is defined as no caloric intake for at least        8 h. Alternatively: 3. 2-h postload glucose >200 mg/di (11.1        mmol/l) during an OGTT. The test should be performed as        described by WHO, using a glucose load containing the equivalent        of 75 g anhydrous glucose dissolved in water.

In the absence of unequivocal hyperglycemia, these criteria should beconfirmed by repeat testing on a different day. The third measure (OGTT)is not recommended for routine clinical use.

(American Diabetes Association, Diagnosis and Classification of DiabetesMellitus, Diabetes Care 2006) However, an increase in the blood sugarlevels or a decrease in the available insulin are rather downstreamdevelopments in the development and progression of diabetes. Alternativediagnostic measures or diagnostic measures which would even identifyindividuals at risk before the early onset of the disease or at least inan early state of the disease are not yet available.

Accordingly, the technical problem underlying the present invention mustbe seen as the provision of means and methods for efficiently andreliably diagnosing a predisposition for diabetes. The technical problemis solved by the embodiments characterized in the claims and describedherein below.

Accordingly, the present invention relates to a method for diagnosing apredisposition for diabetes comprising:

-   (a) determining at least one metabolite in a test sample of a    subject suspected to suffer from diabetes or to have a    predisposition therefor, said at least one metabolite being selected    from the group consisting of: cryptoxanthin, 2-hydroxy-palmitic    acid, triacylglyceride (C16:0,C18:1,C18:2), gondoic acid,    tricosanoic acid, 5-Oxoproline; and-   (b) comparing the results of the determination in step (a) to a    reference, whereby a predisposition for diabetes is to be diagnosed.

Each of said metabolites is a suitable biomarker by its own for thepredisposition for diseases referred to herein. However, mostpreferably, a group of biomarkers including or consisting of thebiomarkers of one of the aforementioned group is to be determined by themethod of the present invention. A group of biomarkers consists,preferably, of at least two, at least three, at least four and,preferably, up to all of the aforementioned biomarkers.

The expression “method for diagnosing” as referred to in accordance withthe present invention means that the method may essentially consist ofthe aforementioned steps or may include further steps. However, it is tobe understood that the method, in a preferred embodiment, is a methodcarried out in vitro, i.e. not practised on the human or animal body.Diagnosing as used herein refers to assessing the probability accordingto which a subject will have a predisposition for the diseases referredto herein, i.e. diabetes. Diagnosis of a predisposition may sometimes bereferred to as prognosis or prediction of the likelihood that a subjectwill develop the disease. As will be understood by those skilled in theart, such an assessment, although preferred to be, may usually not becorrect for 100% of the subjects to be diagnosed. The term, however,requires that a statistically significant portion of subjects can beidentified as having a predisposition for the diseases referred toherein. Whether a portion is statistically significant can be determinedwithout further ado by the person skilled in the art using various wellknown statistic evaluation tools, e.g., determination of confidenceintervals, p-value determination, Student's t-test, Mann-Whitney test,etc. Details are found in Dowdy and Wearden, Statistics for Research,John Wiley & Sons, New York 1983. Preferred confidence intervals are atleast 50%, at least 60%, at least 70%, at least 80%, at least 90%, atleast 95%. The p-values are, preferably, 0.2, 0.1, 0.05.

Diagnosing according to the present invention includes monitoring,confirmation, and classification of the predisposition for the relevantdisease or its symptoms. Monitoring relates to keeping track of analready diagnosed predispostion (for the disease), or a complication,c.g. to analyze the progression of the disease, the influence of aparticular treatment on the progression of disease or complicationsarising during the disease period or after successful treatment of thedisease. Confirmation relates to the strengthening or substantiating adiagnosis already performed using other indicators or markers.Classification relates to allocating the diagnosis according to thestrength or kind of symptoms into different classes, e.g. the diabetestypes as set forth elsewhere in the description. Specifically, subjectscan be, preferably, classified for different risk groups based on themetabolites and the kind of regulation as shown in the Tables, below.The metabolite biomarkers may be indicative for subjects having anincreased risk for diabetes and falling into the risk group of impairedfasting glucose (IFG), impaired glucose tolerance (IGT) or both(IFG&IGT) as indicated in the Tables, below. Thus, preferably, themethod of the present invention is a method for diagnosing whether asubject has a predisposition for diabetes or suffers from IFG, IGT orIFG&IGT based on the metabolites being associated with the saidincreased risk as listed in the accompanying Tables.

The term “diabetes” or “diabetes mellitus” as used herein refers todisease conditions in which the glucose metabolism is impaired. Saidimpairment results in hyperglycaemia. According to the World HealthOrganisation (WHO), diabetes can be subdivided into four classes. Type 1diabetes is caused by a lack of insulin. Insulin is produced by the socalled pancreatic islet cells. Said cells may be destroyed by anautoimmune reaction in Type 1 diabetes (Type 1 a). Moreover, Type 1diabetes also encompasses an idiopathic variant (Type 1b). Type 2diabetes is caused by an insulin resistance. Type 3 diabetes, accordingto the current classification, comprises all other specific types ofdiabetes mellitus. For example, the beta cells may have genetic defectsaffecting insulin production, insulin resistance may be causedgenetically or the pancreas as such may be destroyed or impaired.Moreover, hormone deregulation or drugs may also cause Type 3 diabetes.Type 4 diabetes may occur during pregnancy. Preferably, diabetes as usedherein refers to diabetes Type 2. According to the German Society forDiabetes, diabetes is diagnosed either by a plasma glucose level beinghigher than 110 mg/dl in the fasting state or being higher than 220mg/dl postprandial. Further preferred diagnostic techniques aredisclosed elsewhere in this specification. Further symptoms of diabetesare well known in the art and are described in the standard text booksof medicine, such as Stedman or Pschyrembl.

The term “predisposition” as used herein means that a subject has notyet developed the disease or any of the aforementioned disease symptomsor other diagnostic criteria but, nevertheless, will develop the diseasewithin a defined time window in the future (predictive window) with acertain likelihood. The predictive window is an interval in which thesubject shall develop the disease or condition according to thepredicted probability. The predictive window may be the entire remaininglifespan of the subject upon analysis by the method of the presentinvention. Preferably, however, the predictive window is an interval ofone month, six months or one, two, three, four, five or ten years afterthe sample to be analyzed by the method of the present invention hasbeen obtained. The likelihood for developing the diseases referred toherein shall be significantly larger for a subject having apredisposition than the likelihood of statistical appearance of diabetesmellitus within a given cohort of subjects. Preferably, for anindividual subject the likelihood associated with a predisposition fordeveloping diabetes is at least 30%, at least 40%, at least 50%, atleast 60%, at least 70%, at least 80%, at least 90% or 100% or at least1.5-times, 2-times, 3-times, 4-times, 5-times or 10-times increasedcompared with the average likelihood for a subject of a given cohort fordeveloping diabetes. A cohort of subjects as referred to herein means aplurality of individual subjects which are of the same species and,preferably, are of the same or genetic background or ethnical group and,more preferably, also of the same age and gender.

The term “at least one metabolite” as used herein refers to a singlemetabolite or to a plurality of metabolites, i.e. preferably at least 2,3, 4, 5, 10, 50, 100, 500, 1,000, 2,000, 3,000, 5,000 or 10,000metabolites. It is to be understood that “metabolite” as used herein maybe at least one molecule of said metabolite up to a plurality ofmolecules of the metabolite and that a plurality of metabolites means aplurality of chemically different molecules wherein for each metaboliteat least one molecule up to a plurality of molecules may be present. Ametabolite in accordance with the present invention encompasses allclasses of organic or inorganic chemical compounds including those beingcomprised by biological material such as organisms. Preferably, themetabolite in accordance with the present invention is a small moleculecompound. More preferably, in case a plurality of metabolites isenvisaged, said plurality of metabolites representing a metabolome, i.e.the collection of metabolites being comprised by an organism, an organ,a tissue or a cell at a specific time and under specific conditions.

The metabolites are small molecule compounds, such as substrates forenzymes of metabolic pathways, intermediates of such pathways or theproducts obtained by a metabolic pathway. Metabolic pathways are wellknown in the art and may vary between species. Preferably, said pathwaysinclude at least citric acid cycle, respiratory chain, photosynthesis,photorespiration, glycolysis, gluconeogenesis, hexose monophosphatepathway, oxidative pentose phosphate pathway, production and β-oxidationof fatty acids, urea cycle, amino acid biosynthesis pathways, proteindegradation pathways such as proteasomal degradation, amino aciddegrading pathways, biosynthesis or degradation of: lipids, polyketides(including e.g. flavonoids and isoflavonoids), isoprenoids (includinge.g. terpenes, sterols, steroids, carotenoids, xanthophylls),carbohydrates, phenylpropanoids and derivatives, alcaloids, benzenoids,indoles, indole-sulfur compounds, porphyrines, anthocyans, hormones,vitamins, cofactors such as prosthetic groups or electron carriers,lignin, glucosinolates, purines, pyrimidines, nucleosides, nucleotidesand related molecules such as tRNAs, microRNAs (miRNA) or mRNAs.Accordingly, small molecule compound metabolites are preferably composedof the following classes of compounds: alcohols, alkanes, alkenes,alkines, aromatic compounds, ketones, aldehydes, carboxylic acids,esters, amines, imines, amides, cyanides, amino acids, peptides, thiols,thioesters, phosphate esters, sulfate esters, thioethers, sulfoxides,ethers, or combinations or derivatives of the aforementioned compounds.The small molecules among the metabolites may be primary metaboliteswhich are required for normal cellular function, organ function oranimal growth, development or health. Moreover, small moleculemetabolites further comprise secondary metabolites having essentialecological function, e.g. metabolites which allow an organism to adaptto its environment. Furthermore, metabolites are not limited to saidprimary and secondary metabolites and further encompass artificial smallmolecule compounds. Said artificial small molecule compounds are derivedfrom exogenously provided small molecules which are administered ortaken up by an organism but are not primary or secondary metabolites asdefined above. For instance, artificial small molecule compounds may bemetabolic products obtained from drugs by metabolic pathways of theanimal. Moreover, metabolites further include peptides, oligopeptides,polypeptides, oligonucleotides and polynucleotides, such as RNA or DNA.More preferably, a metabolite has a molecular weight of 50 Da (Dalton)to 30,000 Da, most preferably less than 30,000 Da, less than 20,000 Da,less than 15,000 Da, less than 10,000 Da, less than 8,000 Da, less than7,000 Da, less than 6,000 Da, less than 5,000 Da, less than 4,000 Da,less than 3,000 Da, less than 2,000 Da, less than 1,000 Da, less than500 Da, less than 300 Da, less than 200 Da, less than 100 Da.Preferably, a metabolite has, however, a molecular weight of at least 50Da. Most preferably, a metabolite in accordance with the presentinvention has a molecular weight of 50 Da up to 1,500 Da.

It will be understood that in addition to the aforementioned metabolitesor groups of metabolites, an additional metabolite or a group ofadditional metabolites may be determined by the method of the presentinvention as well. Said additional metabolite or group thereof mayinclude metabolites known to be associated with diabetes orpredisposition for diabetes. Preferably, said additional metabolite isGlucose.

Other preferred metabolites to be determined together, i.e. eithersimultaneously or consecutively, with the aforementioned metabolites orgroups of metabolites are metabolites selected from the group consistingof:

-   (i) a long-chain saturated fatty acid, preferably, Lignoceric acid    (C24:0), Melissic acid (C30:0), or Tricosanoic acid (C23:0),-   (ii) a poly-unsaturated fatty acid, preferably, Docosahexaenoic acid    (C22:cis[4,7,10,13,16,19]6), Eicosapentaenoic acid    (C20:cis[5,8,11,14,17]5), Arachidonic acid (C20:cis-[5,8,11,14]4),    Linoleic acid (C18:cis[9,12]2), or Linolenic acid    (C18:cis[9,12,15]3),-   (iii) an amino acid, preferably, Lysine, Alanine, Threonine,    Tryptophane, Valine, Isoleucine, Leucine, Cysteine, Methionine,    Tyrosine, Phenylalanine, Glycine, Proline, or Glutamine,-   (iv) an antioxidant, preferably, Ascorbic acid, Coenzyme Q10, or    alpha-Tocopherol,-   (v) a metabolite of the Citric Acid Cycle, preferably, Pyruvate,    Citrate, or Malate,-   (vi) a metabolite of the Urea Cycle, preferably, Urea, Citrulline,    Succinate, or Ornithine,-   (vii) Mannose, alpha-Ketoisocaproic acid, Glycerol, lipid fraction,    or 3-Hydroxybutyric acid,-   (viii) glucose.

A “long chain saturated fatty acid” as referred to in accordance withthe present invention encompasses, preferably, C18 to C30 fatty acidswherein the numbers “18” and “30” indicate the number of carbon atoms inthe fatty acid chain. More preferably, it relates to C20 to C30 fattyacids, and, most preferably to Lignoceric acid (C24:0), Melissic acid(C30:0), or Tricosanoic acid (C23:0).

A “poly-unsaturated fatty acid” as used herein means a fatty acidcomprising more than one unsaturated carbon bond. Poly unsaturated fattyacids preferably envisaged by the present invention are C18 to C22 polyunsaturated fatty acids, and, most preferably, Docosahexaenoic acid(C22:cis[4,7,10,13,16,19]6), Eicosapentaenoic acid(C20:cis[5,8,11,14,17]5), Arachidonic acid (C20:cis-[5,8,11,14]4),Linoleic acid (C18:cis[9,12]2), or Linolenic acid (C18:cis[9,12,15]3).

The term “amino acid” as used herein encompasses the natural occurringamino acids as well as derivatives thereof. The naturally occurringamino acids are well known in the art and are described in standard textbooks of biochemistry. More preferably, the term relates to Lysine,Alanine, Threonine, Tryptophane, Valine, Isoleucine, Leucine, Cysteine,Methionine, Tyrosine, Phenylalanine, Glycine, Proline, or Glutamine.

The term “antioxidant” as used herein encompasses compounds which arecapable of preventing oxidation in a subject. Preferably, the termrelates to naturally occurring metabolites which may serve as coenzymesin the cell of a subject or which are vitamins including those whichneed to be exogenously supplied. More preferably, an anti-oxidantaccording to the present invention is Ascorbic acid, Coenzyme Q10, oralpha-Tocopherol.

The term “a metabolite of the Citric Acid Cycle” or “a metabolite of theUrea Cycle” relates to the products, intermediates and reactants whichare synthesized or used as substrates in the aforementioned well knownbiochemical conversion cascades. Those products, intermediates orreactants are described in the biochemical standard text books and arewell known to those skilled in the art. Preferably, Pyruvate, Citrate,or Malate are a metabolite of the Citric Acid Cycle. Urea, Citrulline,Succinate, or Ornithine are, preferably, a metabolite of the Urea Cyclereferred to herein.

Preferably, a group of biomarkers is determined in accordance with themethod of the present invention. More preferably, said group consists ofbiomarkers from different metabolite groups specified above under (i) to(vii). Most preferably, at least one metabolite of at least two, atleast three, at least four, at least five, at least six or all of theaforementioned groups (i) to (vii) is to be determined. It has beenfound that the members of the aforementioned metabolite classes providesupportive biomarkers for diagnosing diabetes or a predisposition fordiabetes. Moreover, a combination of the aforementioned metaboliteclasses provides even more superior and reliable results.

Also preferably, in addition to the aforementioned supportivemetabolites or groups of supportive metabolites at least one supportivemetabolite is determined selected from any of the following groupsconsisting of: 3-hydroxybutyric acid, alanine, alpha-ketoisocaproicacid, alpha-tocopherol, arginine, ascorbic acid, aspargine,beta-carotene, cholestenol, citrate, citrulline, creatinine,eicosapentaenoic acid (C20:cis[5,8,11,14,17]5), folate, glucose,glucose-1-phosphate, glutamate, glutamine, glyceric acid,glycerol-3-phosphate, glycine, isoleucine, lactate, leucine, malate,mannose, methionine, myo-inositol, ornithine, palmitic acid,phospholipids, pregnenolone sulfate, stearic acid, succinate, threonicacid, threonine, triacylglycerides, tryptophane, uric acid, and valine.

More preferably, the at least one supportive metabolite is selected fromthe group consisting of: tryptophane, alanine, leucine, palmitic acid,eicosatrienoic acid, glycerophospholipids, isoleucine, eicosatrienoicacid, tryptophane, lignoceric acid, linoleic acid, serine, tyrosine,linoleic acid, pregnenolone sulfate, aspartate, arachidonic acid, andsuccinate. Most preferably, the subject in said case is a male.

More preferably, the at least one supportive metabolite is selected fromthe group consisting of: alanine, palmitic acid, isoleucine,eicosatrienoic acid, uric acid, stearic acid, and serine. Mostpreferably, the subject in said case is a female.

Each of said metabolites is a suitable supportive biomarker by its ownfor the predisposition of the diseases referred to herein. However, mostpreferably, a group of supportive biomarkers including or consisting ofthe biomarkers of one of the aforementioned groups is to be determinedby the method of the present invention. A group of biomarkers consists,preferably, of at least two, at least three, at least four and,preferably, up to all of the aforementioned supportive biomarkers.

The supportive metabolites referred to before will, preferably, also becompared to suitable reference results as specified elsewhere herein.The result of the said comparison will be further supportive for thefinding as to whether the subject will have a predisposition for thediseases referred to herein or not. Preferred reference results, valuesfor changes of the relative amounts and indications for the kind ofregulation are to be found in the accompanying Examples, below.

The term “test sample” as used herein refers to samples to be used forthe diagnosis of a predisposition for diabetes by the method of thepresent invention. Said test sample is a biological sample. Samples frombiological sources (i.e. biological samples) usually comprise aplurality of metabolites. Preferred biological samples to be used in themethod of the present invention are samples from body fluids,preferably, blood, plasma, serum, lymph, sudor, saliva, tears, sperm,vaginal fluid, faeces, urine or cerebrospinal fluid, or samples derived,e.g., by biopsy, from cells, tissues or organs. This also encompassessamples comprising subcellular compartments or organelles, such as themitochondria, Golgi network or peroxisomes. Moreover, biological samplesalso encompass gaseous samples, such as volatiles of an organism.Biological samples are derived from a subject as specified elsewhereherein. Techniques for obtaining the aforementioned different types ofbiological samples are well known in the art. For example, blood samplesmay be obtained by blood taking while tissue or organ samples are to beobtained, e.g., by biopsy.

The aforementioned samples are, preferably, pre-treated before they areused for the method of the present invention. As described in moredetail below, said pre-treatment may include treatments required torelease or separate the compounds or to remove excessive material orwaste. Suitable techniques comprise centrifugation, extraction,fractioning, purification and/or enrichment of compounds. Moreover,other pre-treatments are carried out in order to provide the compoundsin a form or concentration suitable for compound analysis. For example,if gas-chromatography coupled mass spectrometry is used in the method ofthe present invention, it will be required to derivatize the compoundsprior to the said gas chromatography. Suitable and necessarypre-treatments depend on the means used for carrying out the method ofthe invention and are well known to the person skilled in the art.Pre-treated samples as described before are also comprised by the term“sample” as used in accordance with the present invention.

The term “subject” as used herein relates to animals, preferably tomammals such as mice, rats, sheep, dogs, cats, horses, monkeys, or cowsand, also preferably, to humans. Other animals which may be diagnosedapplying the method of the present invention are birds or reptiles. Asubject suspected to suffer from diabetes or to have a predispositiontherefor as used herein refers to a subject which shows, preferably,symptoms or clinical signs or parameters indicative for diabetes.However, the term also relates to an apparently healthy subject, i.e. asubject not exhibiting any of the aforementioned symptoms, clinicalsigns or parameters. Apparently healthy subjects may by investigated bythe method of the present invention as a measure of preventive care orfor population screening purposes.

The term “determining said at least one metabolite” as used hereinrefers to determining at least one characteristic feature of the atleast one metabolite comprised by the sample referred to herein.Characteristic features in accordance with the present invention arefeatures which characterize the physical and/or chemical propertiesincluding biochemical properties of a metabolite. Such propertiesinclude, e.g., molecular weight, viscosity, density, electrical charge,spin, optical activity, colour, fluorescence, chemoluminescence,elementary composition, chemical structure, capability to react withother compounds, capability to elicit a response in a biological readout system (e.g., induction of a reporter gene) and the like. Values forsaid properties may serve as characteristic features and can bedetermined by techniques well known in the art. Moreover, thecharacteristic feature may be any feature which is derived from thevalues of the physical and/or chemical properties of a metabolite bystandard operations, e.g., mathematical calculations such asmultiplication, division or logarithmic calculus. Most preferably, theat least one characteristic feature allows the determination and/orchemical identification of the said at least one metabolite.

The at least one metabolite comprised by a test sample may be determinedin accordance with the present invention quantitatively orqualitatively. For qualitative determination, the presence or absence ofthe metabolite will be determined by a suitable technique. Moreover,qualitative determination may, preferably, include determination of thechemical structure or composition of the metabolite. For quantitativedetermination, either the precise amount of the at least one metabolitepresent in the sample will be determined or the relative amount of theat least one metabolite will be determined, preferably, based on thevalue determined for the characteristic feature(s) referred to hereinabove. The relative amount may be determined in a case were the preciseamount of a metabolite can or shall not be determined. In said case, itcan be determined whether the amount in which the metabolite is presentis enlarged or diminished with respect to a second sample comprisingsaid metabolite in a second amount. Quantitatively analysing ametabolite, thus, also includes what is sometimes referred to assemi-quantitative analysis of a metabolite.

Moreover, determining as used in the method according to the presentinvention, preferably, includes using a compound separation step priorto the analysis step referred to before. Preferably, said compoundseparation step yields a time resolved separation of the metabolitescomprised by the sample. Suitable techniques for separation to be usedpreferably in accordance with the present invention, therefore, includeall chromatographic separation techniques such as liquid chromatography(LC), high performance liquid chromatography (HPLC), gas chromatography(GC), thin layer chromatography, size exclusion or affinitychromatography. These techniques are well known in the art and can beapplied by the person skilled in the art without further ado. Mostpreferably, LC and/or GC are chromatographic techniques to be envisagedby the method of the present invention. Suitable devices for suchdetermination of metabolites are well known in the art. Preferably, massspectrometry is used in particular gas chromatography mass spectrometry(GC-MS), liquid chromatography mass spectrometry (LC-MS), directinfusion mass spectrometry or Fourier transform ion-cyclotrone-resonancemass spectrometry (FT-ICR-MS), capillary electrophoresis massspectrometry (CE-MS), high-performance liquid chromatography coupledmass spectrometry (HPLC-MS), quadrupole mass spectrometry, anysequentially coupled mass spectrometry, such as MS-MS or MS-MS-MS,inductively coupled plasma mass spectrometry (ICP-MS), pyrolysis massspectrometry (Py-MS), ion mobility mass spectrometry or time of flightmass spectrometry (TOF). Most preferably, LC-MS and/or GC-MS are used asdescribed in detail below. Said techniques are disclosed in, e.g.,Nissen, Journal of Chromatography A, 703, 1995: 37-57, U.S. Pat. No.4,540,884 or U.S. Pat. No. 5,397,894, the disclosure content of which ishereby incorporated by reference. As an alternative or in addition tomass spectrometry techniques, the following techniques may be used forcompound determination: nuclear magnetic resonance (NMR), magneticresonance imaging (MRI), Fourier transform infrared analysis (FT-IR),ultra violet (UV) spectroscopy, refraction index (RI), fluorescentdetection, radiochemical detection, electrochemical detection, lightscattering (LS), dispersive Raman spectroscopy or flame ionisationdetection (FID). These techniques are well known to the person skilledin the art and can be applied without further ado. The method of thepresent invention shall be, preferably, assisted by automation. Forexample, sample processing or pre-treatment can be automated byrobotics. Data processing and comparison is, preferably, assisted bysuitable computer programs and databases. Automation as described hereinbefore allows using the method of the present invention inhigh-throughput approaches.

Moreover, the at least one metabolite can also be determined by aspecific chemical or biological assay. Said assay shall comprise meanswhich allow to specifically detect the at least one metabolite in thesample. Preferably, said means are capable of specifically recognizingthe chemical structure of the metabolite or are capable of specificallyidentifying the metabolite based on its capability to react with othercompounds or its capability to elicit a response in a biological readout system (e.g., induction of a reporter gene). Means which are capableof specifically recognizing the chemical structure of a metabolite are,preferably, antibodies or other proteins which specifically interactwith chemical structures, such as receptors or enzymes. Specificantibodies, for instance, may be obtained using the metabolite asantigen by methods well known in the art. Antibodies as referred toherein include both polyclonal and monoclonal antibodies, as well asfragments thereof, such as Fv, Fab and F(ab)₂ fragments that are capableof binding the antigen or hapten. The present invention also includeshumanized hybrid antibodies wherein amino acid sequences of a non-humandonor antibody exhibiting a desired antigen-specificity are combinedwith sequences of a human acceptor antibody. Moreover, encompassed aresingle chain antibodies. The donor sequences will usually include atleast the antigen-binding amino acid residues of the donor but maycomprise other structurally and/or functionally relevant amino acidresidues of the donor antibody as well. Such hybrids can be prepared byseveral methods well known in the art. Suitable proteins which arecapable of specifically recognizing the metabolite are, preferably,enzymes which are involved in the metabolic conversion of the saidmetabolite. Said enzymes may either use the metabolite as a substrate ormay convert a substrate into the metabolite. Moreover, said antibodiesmay be used as a basis to generate oligopeptides which specificallyrecognize the metabolite. These oligopeptides shall, for example,comprise the enzyme's binding domains or pockets for the saidmetabolite. Suitable antibody and/or enzyme based assays may be RIA(radioimmunoassay), ELISA (enzyme-linked immunosorbent assay), sandwichenzyme immune tests, electrochemiluminescence sandwich immunoassays(ECLIA), dissociation-enhanced lanthanide fluoro immuno assay (DELFIA)or solid phase immune tests. Moreover, the metabolite may also beidentified based on its capability to react with other compounds, i.e.by a specific chemical reaction. Suitable reactions are well known inthe art and, preferably encompass enzymatic reactions (e.g for mannosePitkanen E, Pitkanen O, Uotila L.; Eur J Clin Chem Clin Biochem. 1997October; 35(10):761-6; or ascorbic acid Winnie Lee, Susan M. Roberts andRobert F. Labbe; Clinical Chemistry 43: 154-157, 1997), enzymaticspectrophotometric methods (BN La Du, RR Howell, P J Michael and E KSober; Pediatrics, January 1963, 39-46, Vol 31, No. 1),spectrofluorimetric methods (Sumi T, Umeda Y, Kishi Y, Takahashi K,Kakimoto F.; Clin Chim Acta. 1976 Dec. 1; 73(2):233-9) and fluorescence;chemiluminescence (J. J. Thiele, H. J. Freisleben, J. Fuchs and F. R.Ochsendorf; Human Reproduction, Vol. 10, No. 1, pp. 110-115, 1995).Further detection methods such as capillary electrophoresis (Hubert A.Carchon and Jaak Jaeken; Clinical Chemistry 47: 1319-1321, 2001) andcolorimetric methods (Kyaw A; Clin Chim Acta. 1978 June; 86(2):153-7)can be used. Further, the metabolite may be determined in a sample dueto its capability to elicit a response in a biological read out system.The biological response shall be detected as read out indicating thepresence and/or the amount of the metabolite comprised by the sample.The biological response may be, e.g., the induction of gene expressionor a phenotypic response of a cell or an organism.

Further, it is to be understood that depending of the technique used fordetermining the said at least one metabolite, the analyte which will bedetected could be a derivative of the physiologically occurringmetabolite, i.e. the metabolite present within a subject. Such analytesmay be generated as a result of sample preparation or detection means.The compounds referred to herein are deemed to be analytes. However, asset forth above, these analytes will represent the metabolites in aqualitative and quantitative manner. Moreover, it is to be understoodthat for a plurality of metabolites, the metabolite will be identical tothe analyte.

The term “reference” refers to results, i.e. data of characteristicfeatures of the at least one metabolite, which can be correlated to apredispostion for diabetes. Such reference results are, preferably,obtained from a sample from a subject known to have predisposition fordiabetes. The reference results may be obtained by applying the methodof the present invention. Alternatively, but nevertheless alsopreferred, the reference results may be obtained from sample of asubject known not to have a predisposition for diabetes, i.e. which willnot develop diabetes in the future, and, more preferably, other diseasesas well. Moreover, the reference, also preferably, could be a calculatedreference, most preferably the average or median, for the relative orabsolute amount of a metabolite of a population of individuals(comprising the subject to be investigated). The absolute or relativeamounts of the metabolites of said individuals of the population can bedetermined as specified elsewhere herein. How to calculate a suitablereference value, preferably, the average or median, is well known in theart. The population of subjects referred to before shall comprise aplurality of subjects, preferably, at least 5, 10, 50, 100, 1,000 or10,000 subjects. It is to be understood that the subject to be diagnosedby the method of the present invention and the subjects of the saidplurality of subjects are of the same species.

More preferably, the reference results, i.e. values for at least onecharacteristic features of the at least one metabolite, will be storedin a suitable data storage medium such as a database and are, thus, alsoavailable for future diagnoses. This also allows efficiently diagnosingpredisposition for a disease because suitable reference results can beidentified in the database once it has been confirmed (in the future)that the subject from which the corresponding reference sample wasobtained (indeed) developed diabetes. Preferred reference results whichare associated with diabetes or predisposition therefor in humans arethose shown in the Tables of the accompanying Examples.

The term “comparing” refers to assessing whether the results of thedetermination described hereinabove in detail, i.e. the results of thequalitative or quantitative determination of the at least onemetabolite, are identical or similar to reference results or differtherefrom.

In case the reference results are obtained from a subject known to havea predisposition for diabetes, the said predisposition can be diagnosedbased on the degree of identity or similarity between the test resultsobtained from the test sample and the aforementioned reference results,i.e. based on an identical or similar qualitative or quantitativecomposition with respect to the at least one metabolite. The results ofthe test sample and the reference results are identical, if the valuesfor the characteristic features and, in the case of quantitativedetermination, the intensity values are identical. Said results aresimilar, if the values of the characteristic features are identical butthe intensity values are different. Such a difference is, preferably,not significant and shall be characterized in that the values for theintensity are within at least the interval between 1^(st) and 99^(th)percentile, 5^(th) and 95^(th) percentile, 10^(th) and 90^(th)percentile, 20^(th) and 80^(th) percentile, 30^(th) and 70^(th)percentile, 40^(th) and 60^(th) percentile of the reference value. the50^(th), 60^(th), 70^(th), 80^(th), 90^(th) or 95^(th) percentile of thereference value.

In case the reference results are obtained from a subject or a group ofsubjects known not to have a predisposition for diabetes, the saidpredisposition can be diagnosed based on the differences between thetest results obtained from the test sample and the aforementionedreference results, i.e. differences in the qualitative or quantitativecomposition with respect to the at least one metabolite. The sameapplies if a calculated reference as specified above is used. Thedifference may be an increase in the absolute or relative amount of ametabolite (sometimes referred to as up-regulation of the metabolite;see also Examples) or a decrease in either of said amounts or theabsence of a detectable amount of the metabolite (sometimes referred toas up-regulation of the metabolite; see also Examples). Preferably, thedifference in the relative or absolute amount is significant, i.e.outside of the interval between 45^(th) and 55^(th) percentile, 40^(th)and 60^(th) percentile, 30^(th) and 70^(th) percentile, 20^(th) and80^(th) percentile, 10^(th) and 90^(th) percentile, 5^(th) and 95^(th)percentile, 1^(st) and 99^(th) percentile of the reference value. Forthe specific metabolites referred to in this specification elsewhere,preferred values for the changes in the relative amounts (i.e.“fold”-changes) or the kind of change (i.e. “up”- or “down”-regulationresulting in a higher or lower relative and/or absolute amount) areindicated in the accompanying Tables, below. If it is indicated in saidtables that a given metabolite is “up-regulated” in a subject, therelative and/or absolute amount will be increased, if it is“down-regulated”, the relative and/or absolute amount of the metabolitewill be decreased. Moreover, the “fold”-change indicates the degree ofincrease or decrease, e.g., a 2-fold increase means that the amount istwice the amount of the metabolite compared to the reference.

Thus, the method of the present invention in a preferred embodimentincludes a reference that is derived from a subject or a group known tohave predisposition for diabetes. Most preferably, identical or similarresults for the test sample and the said reference (i.e. similarrelative or absolute amounts of the at least one metabolite) areindicative for a predisposition for diabetes in that case. In anotherpreferred embodiment of the method of the present invention, thereference is derived from a subject or a group known not to havepredisposition for diabetes or is a calculated reference. Mostpreferably, the absence of the at least one metabolite or an amountwhich, preferably significantly, differs in the test sample incomparison to the reference sample (i.e. a significant difference in theabsolute or relative amount is observed) is indicative for apredisposition for diabetes in such a case.

The comparison is, preferably, assisted by automation. For example, asuitable computer program comprising algorithm for the comparison of twodifferent data sets (e.g., data sets comprising the values of thecharacteristic feature(s)) may be used. Such computer programs andalgorithm are well known in the art. Notwithstanding the above, acomparison can also be carried out manually.

The aforementioned methods for the determination of the at least onemetabolite can be implemented into a device. A device as used hereinshall comprise at least the aforementioned means. Moreover, the device,preferably, further comprises means for comparison and evaluation of thedetected characteristic feature(s) of the at least one metabolite and,also preferably, the determined signal intensity. The means of thedevice are, preferably, operatively linked to each other. How to linkthe means in an operating manner will depend on the type of meansincluded into the device. For example, where means for automaticallyqualitatively or quantitatively determining the metabolite are applied,the data obtained by said automatically operating means can be processedby, e.g., a computer program in order to facilitate the diagnosis.Preferably, the means are comprised by a single device in such a case.Said device may accordingly include an analyzing unit for themetabolites and a computer unit for processing the resulting data forthe diagnosis. Alternatively, where means such as test stripes are usedfor determining the metabolites, the means for diagnosing may comprisecontrol stripes or tables allocating the determined result data toresult data known to be accompanied with a predisposition for diabetesor those being indicative for a subject without such a predisposition asdiscussed above. Preferred devices are those which can be appliedwithout the particular knowledge of a specialized clinician, e.g., teststripes or electronic devices which merely require loading with asample.

Alternatively, the methods for the determination of the at least onemetabolite can be implemented into a system comprising several deviceswhich are, preferably, operatively linked to each other. Specifically,the means must be linked in a manner as to allow carrying out the methodof the present invention as described in detail above. Therefore,operatively linked, as used herein, preferably, means functionallylinked. Depending on the means to be used for the system of the presentinvention, said means may be functionally linked by connecting each meanwith the other by means which allow data transport in between saidmeans, e.g., glass fiber cables, and other cables for high throughputdata transport. Nevertheless, wireless data transfer between the meansis also envisaged by the present invention, e.g., via LAN (Wireless LAN,WLAN). A preferred system comprises means for determining metabolites.Means for determining metabolites, as used herein, encompass means forseparating metabolites, such as chromatographic devices, and means formetabolite determination, such as mass spectrometry devices. Suitabledevices have been described in detail above. Preferred means forcompound separation to be used in the system of the present inventioninclude chromatographic devices, more preferably devices for liquidchromatography, HPLC, and/or gas chromatography. Preferred devices forcompound determination comprise mass spectrometry devices, morepreferably, GC-MS, LC-MS, direct infusion mass spectrometry, FT-ICR-MS,CE-MS, HPLC-MS, quadrupole mass spectrometry, sequentially coupled massspectrometry (including MS-MS or MS-MS-MS), ICP-MS, Py-MS or TOF. Theseparation and determination means are, preferably, coupled to eachother. Most preferably, LC-MS and/or GC-MS is used in the system of thepresent invention as described in detail elsewhere in the specification.Further comprised shall be means for comparing and/or analyzing theresults obtained from the means for determination of metabolites. Themeans for comparing and/or analyzing the results may comprise at leastone databases and an implemented computer program for comparison of theresults. Preferred embodiments of the aforementioned systems and devicesare also described in detail below.

Advantageously, it has been found in accordance with the presentinvention that the at least one of the aforementioned metabolites willbe a suitable biomarker for a predisposition for diabetes. Applyingthese metabolites as biomarkers allows a rapid, reliable andcost-effective diagnosis of a predisposition for diabetes. Moreover, themethod can be assisted by automation as described elsewhere in thisdescription and, thus, allows high-throughput screening for subjectsbeing at risk of suffering from diabetes. Thereby, the method of thepresent invention may assist health programs for diabetes prevention andcan be used to monitor success of preventive therapies for diabetes orother measures for the prevention of diabetes including nutritionaldiets. Moreover, the metabolites or combinations of metabolites referredto herein can be determined simultaneously in a time and cost effectivemanner by the metabolic profiling techniques described in thisspecification. Furthermore, the method of the present invention allowsto asses the risk for a subject for being or becoming a member of acertain diabetes risk group, i.e. IFG, IGT or IFG&IGT. The reportedprevalence of IFG and IGT varies widely between 5 and 26° A) dependingon ethnic group, age and sex distribution. Both risk groups, IFG and IGTare expected to increase in the near future. For both, IFG or IGT riskgroups, a 25% progressing to incident diabetes is reported within 3-5years, with 50% remaining in their abnormal glycemic state and 25%reverting to normal glucose levels. With longer observation; themajority of individuals with IFG or IGT appear to develop diabetes.Individuals with both IFG and IGT (IFG&IGT) have approximately doublethe rate of developing diabetes compared with subjects have either IFGor IGT (Nathan 2007, Diabetes Care 30(3): 753-759).

The explanations and interpretations of the terms made above applyaccordingly to the other embodiments specified herein below.

In a preferred embodiment of the method of the present invention said atleast one metabolite is selected from the group consisting of:diacylglyceride (C18:1,C18:2) and triacylglyceride (C16:0,C18:2,C18:2).

Each of said metabolites is a suitable biomarker by its own for thepredisposition referred to herein. However, most preferably, a group ofbiomarkers including biomarkers of one of the aforementioned groups isto be determined by the method of the present invention. A group ofbiomarkers consists, preferably, of at least two, at least three, atleast four and, preferably, up to all of the aforementioned biomarkers.Furthermore, it has been found in the study underlying the presentinvention that the metabolites of the aforementioned groups areparticularly well-suited as biomarkers for a predisposition for diabetesin male individuals. Accordingly, the subject to be diagnosed inaccordance with the present invention is in the context with theaforementioned preferred embodiment, more preferably a male subject.

As described above, in a preferred embodiment of the method of thepresent invention, said determining of the at least one metabolitecomprises mass spectrometry (MS). Mass spectrometry as used hereinencompasses all techniques which allow for the determination of themolecular weight (i.e. the mass) or a mass variable corresponding to acompound, i.e. a metabolite, to be determined in accordance with thepresent invention. Preferably, mass spectrometry as used herein relatesto GC-MS, LC-MS, direct infusion mass spectrometry, FT-ICR-MS, CE-MS,HPLC-MS, quadrupole mass spectrometry, any sequentially coupled massspectrometry such as MS-MS or MS-MS-MS, ICP-MS, Py-MS, TOF or anycombined approaches using the aforementioned techniques. How to applythese techniques is well known to the person skilled in the art.Moreover, suitable devices are commercially available. More preferably,mass spectrometry as used herein relates to LC-MS and/or GC-MS, i.e. tomass spectrometry being operatively linked to a prior chromatographicseparation step. More preferably, mass spectrometry as used hereinencompasses quadrupole MS. Most preferably, said quadrupole MS iscarried out as follows: a) selection of a mass/charge quotient (m/z) ofan ion created by ionisation in a first analytical quadrupole of themass spectrometer, b) fragmentation of the ion selected in step a) byapplying an acceleration voltage in an additional subsequent quadrupolewhich is filled with a collision gas and acts as a collision chamber,selection of a mass/charge quotient of an ion created by thefragmentation process in step b) in an additional subsequent quadrupole,whereby steps a) to c) of the method are carried out at least once andanalysis of the mass/charge quotient of all the ions present in themixture of substances as a result of the ionisation process, whereby thequadrupole is filled with collision gas but no acceleration voltage isapplied during the analysis. Details on said most preferred massspectrometry to be used in accordance with the present invention can befound in WO 03/073464.

More preferably, said mass spectrometry is liquid chromatography (LC) MSand/or gas chromatography (GC) MS.

Liquid chromatography as used herein refers to all techniques whichallow for separation of compounds (i.e. metabolites) in liquid orsupercritical phase. Liquid chromatography is characterized in thatcompounds in a mobile phase are passed through the stationary phase.When compounds pass through the stationary phase at different rates theybecome separated in time since each individual compound has its specificretention time (i.e. the time which is required by the compound to passthrough the system). Liquid chromatography as used herein also includesHPLC. Devices for liquid chromatography are commercially available, e.g.from Agilent Technologies, USA. Gas chromatography as applied inaccordance with the present invention, in principle, operates comparableto liquid chromatography. However, rather than having the compounds(i.e. metabolites) in a liquid mobile phase which is passed through thestationary phase, the compounds will be present in a gaseous volume. Thecompounds pass the column which may contain solid support materials asstationary phase or the walls of which may serve as or are coated withthe stationary phase. Again, each compound has a specific time which isrequired for passing through the column. Moreover, in the case of gaschromatography it is preferably envisaged that the compounds arederivatised prior to gas chromatography. Suitable techniques forderivatisation are well known in the art. Preferably, derivatisation inaccordance with the present invention relates to methoxymation andtrimethylsilylation of, preferably, polar compounds andtransmethylation, methoxymation and trimethylsilylation of, preferably,non-polar (i.e. lipophilic) compounds.

Furthermore, the present invention relates to a data collectioncomprising characteristic values of at least one metabolite beingindicative for a predisposition for diabetes, said metabolite beingselected from any one of the groups referred to above.

The term “data collection” refers to a collection of data which may bephysically and/or logically grouped together. Accordingly, the datacollection may be implemented in a single data storage medium or inphysically separated data storage media being operatively linked to eachother. Preferably, the data collection is implemented by means of adatabase. Thus, a database as used herein comprises the data collectionon a suitable storage medium. Moreover, the database, preferably,further comprises a database management system. The database managementsystem is, preferably, a network-based, hierarchical or object-orienteddatabase management system. Furthermore, the database may be a federalor integrated database. More preferably, the database will beimplemented as a distributed (federal) system, e.g. as aClient-Server-System. More preferably, the database is structured as toallow a search algorithm to compare a test data set with the data setscomprised by the data collection. Specifically, by using such analgorithm, the database can be searched for similar or identical datasets being indicative for diabetes or a predisposition thereof (e.g. aquery search). Thus, if an identical or similar data set can beidentified in the data collection, the test data set will be associatedwith a predisposition for diabetes. Consequently, the informationobtained from the data collection can be used to diagnose apredisposition for diabetes based on a test data set obtained from asubject. More preferably, the data collection comprises characteristicvalues of all metabolites comprised by any one of the groups recitedabove.

In light of the foregoing, the present invention encompasses a datastorage medium comprising the aforementioned data collection.

The term “data storage medium” as used herein encompasses data storagemedia which are based on single physical entities such as a CD, aCD-ROM, a hard disk, optical storage media, or a diskette. Moreover, theterm further includes data storage media consisting of physicallyseparated entities which are operatively linked to each other in amanner as to provide the aforementioned data collection, preferably, ina suitable way for a query search.

The present invention also relates to a system comprising:

-   (a) means for comparing characteristic values of metabolites of a    sample operatively linked to-   (b) a data storage medium as described above.

The term “system” as used herein relates to different means which areoperatively linked to each other. Said means may be implemented in asingle device or may be physically separated devices which areoperatively linked to each other. The means for comparing characteristicvalues of metabolites operate, preferably, based on an algorithm forcomparison as mentioned before. The data storage medium, preferably,comprises the aforementioned data collection or database, wherein eachof the stored data sets being indicative for a predisposition fordiabetes. Thus, the system of the present invention allows identifyingwhether a test data set is comprised by the data collection stored inthe data storage medium. Consequently, the system of the presentinvention may be applied as a diagnostic means in diagnosing apredisposition for diabetes.

In a preferred embodiment of the system, means for determiningcharacteristic values of metabolites of a sample are comprised.

The term “means for determining characteristic values of metabolites”preferably relates to the aforementioned devices for the determinationof metabolites such as mass spectrometry devices, NMR devices or devicesfor carrying out chemical or biological assays for the metabolites.

Moreover, the present invention relates to a diagnostic means comprisingmeans for the determination of at least one metabolite selected from anyone of the groups referred to above.

The term “diagnostic means”, preferably, relates to a diagnostic device,system or biological or chemical assay as specified elsewhere in thedescription in detail.

The expression “means for the determination of at least one metabolite”refers to devices or agents which are capable of specificallyrecognizing the metabolite. Suitable devices may be spectrometricdevices such as mass spectrometry, NMR devices or devices for carryingout chemical or biological assays for the metabolites. Suitable agentsmay be compounds which specifically detect the metabolites. Detection asused herein may be a two-step process, i.e. the compound may first bindspecifically to the metabolite to be detected and subsequently generatea detectable signal, e.g., fluorescent signals, chemiluminescentsignals, radioactive signals and the like. For the generation of thedetectable signal further compounds may be required which are allcomprised by the term “means for determination of the at least onemetabolite”. Compounds which specifically bind to the metabolite aredescribed elsewhere in the specification in detail and include,preferably, enzymes, antibodies, ligands, receptors or other biologicalmolecules or chemicals which specifically bind to the metabolites.

Further, the present invention relates to a diagnostic compositioncomprising at least one metabolite selected from any one of the groupsreferred to above.

The at least one metabolite selected from any of the aforementionedgroups will serve as a biomarker, i.e. an indicator molecule for a riskcondition in the subject, i.e. the predisposition of diabetes. Thus, themetabolite molecules itself may serve as diagnostic compositions,preferably, upon visualization or detection by the means referred to inherein. Thus, a diagnostic composition which indicates the presence of ametabolite according to the present invention may also comprise the saidbiomarker physically, e.g., a complex of an antibody and the metaboliteto be detected may serve as the diagnostic composition. Accordingly, thediagnostic composition may further comprise means for detection of themetabolites as specified elsewhere in this description. Alternatively,if detection means such as MS or NMR based techniques are used, themolecular species which serves as an indicator for the risk conditionwill be the at least one metabolite comprised by the test sample to beinvestigated. Thus, the at least one metabolite referred to inaccordance with the present invention shall serve itself as a diagnosticcomposition due to its identification as a biomarker.

Finally, the present invention relates to the use of at least onemetabolite or means for the determination thereof for the manufacture ofa diagnostic device or composition for diagnosing a predisposition fordiabetes, wherein said at least one metabolite is selected from any oneof the groups referred to above.

As specified above already, each of said metabolites is a suitablebiomarker by its own for a predisposition of the diseases referred toherein. However, most preferably, a group of biomarkers includingbiomarkers of any one of the aforementioned groups is to be determinedby the method of the present invention. A group of biomarkers consists,preferably, of at least two, at least three, at least four and,preferably, up to all of the aforementioned biomarkers.

All references referred to above are herewith incorporated by referencewith respect to their entire disclosure content as well as theirspecific disclosure content explicitly referred to in the abovedescription.

The invention will now be illustrated by the following Examples whichare not intended to restrict or limit the scope of this invention.

EXAMPLE 1 Determination of Metabolites

Volunteers were informed about planed examinations. The experimentalprotocol was approved by the Dife (German Institute for Human Nutrition)Institutional Review Board, and all subjects gave written informedconsent. Afterwards anthropometric values and intima media thicknesswere measured. Following these examinations an oral glucose tolerancetest (OGTT) with 75 g glucose was performed. Blood samples were taken at0, 30, 60 and 120 minutes. Volunteers were categorized by criteria ofthe WHO and ADA. Plasma was obtained from whole blood by addition ofEDTA as anticoagulant and subsequent centrifugation.

Samples were prepared and subjected to LCMS and GCMS analysis asdescribed in the following:

The samples were prepared in the following way: Proteins were separatedby precipitation from blood plasma. After addition of water and amixture of ethanol and dichlormethan the remaining sample was fractionedinto an aqueous, polar phase and an organic, lipophilic phase.

For the transmethanolysis of the lipid extracts a mixture of 140 μl ofchloroform, 37 μl of hydrochloric acid (37% by weight HCl in water), 320μl of methanol and 20 μl of toluene was added to the evaporated extract.The vessel was sealed tightly and heated for 2 hours at 100° C., withshaking. The solution was subsequently evaporated to dryness. Theresidue was dried completely.

The methoximation of the carbonyl groups was carried out by reactionwith methoxyamine hydrochloride (20 mg/ml in pyridine, 100 μl for 1.5hours at 60° C.) in a tightly sealed vessel. 20 μl of a solution ofodd-numbered, straight-chain fatty acids (solution of each 0.3 mg/mL offatty acids from 7 to 25 carbon atoms and each 0.6 mg/mL of fatty acidswith 27, 29 and 31 carbon atoms in 3/7 (v/v) pyridine/toluene) wereadded as time standards. Finally, the derivatization with 100 μl ofN-methyl-N-(trimethylsilyl)-2,2,2-trifluoroacetamide (MSTFA) was carriedout for 30 minutes at 60° C., again in the tightly sealed vessel. Thefinal volume before injection into the GC was 220 μl.

For the polar phase the derivatization was performed in the followingway: The methoximation of the carbonyl groups was carried out byreaction with methoxyamine hydrochloride (20 mg/ml in pyridine, 50 μlfor 1.5 hours at 60° C.) in a tightly sealed vessel. 10 μl of a solutionof odd-numbered, straight-chain fatty acids (solution of each 0.3 mg/mLof fatty acids from 7 to 25 carbon atoms and each 0.6 mg/mL of fattyacids with 27, 29 and 31 carbon atoms in 3/7 (v/v) pyridine/toluene)were added as time standards. Finally, the derivatization with 50 μl ofN-methyl-N-(trimethylsilyl)-2,2,2-trifluoroacetamide (MSTFA) was carriedout for 30 minutes at 60° C., again in the tightly sealed vessel. Thefinal volume before injection into the GC was 110 μl.

The GC-MS systems consisted of an Agilent 6890 GC coupled to an Agilent5973 MSD. The autosamplers are CompiPal or GCPal from CTC.

For the analysis usual commercial capillary separation columns (30m×0.25 mm×0.25 μm) with different poly-methyl-siloxane stationary phasescontaining 0% up to 35% of aromatic moieties, depending on the analysedsample materials and fractions from the phase separation step, were used(for example: DB-1 ms, HP-5 ms, DB-XLB, DB-35 ms, Agilent Technologies).Up to 1 μL of the final volume was injected splitless and the oventemperature program was started at 70° C. and ended at 340° C. withdifferent heating rates depending on the sample material and fractionfrom the phase separation step in order to achieve a sufficientchromatographic separation and number of scans within each analyte peak.Furthermore RTL (Retention Time Locking, Agilent Technologies) was usedfor the analysis and usual GC-MS standard conditions, for exampleconstant flow with nominal 1 to 1.7 ml/min. and helium as the mobilephase gas, ionisation was done by electron impact with 70 eV, scanningwithin a m/z range from 15 to 600 with scan rates from 2.5 to 3scans/sec and standard tune conditions.

The HPLC-MS systems consists of an Agilent 1100 LC system (AgilentTechnologies, Waldbronn, Germany) coupled with an API 4000 Massspectrometer (Applied Biosystem/MDS SCIEX, Toronto, Canada). HPLCanalysis was performed on commercially available reversed phaseseparation columns with C18 stationary phases (for example: GROM ODS 7pH, Thermo Betasil C18). Up to 10 μL of the final sample volume wasinjected and separation was performed with gradient elution usingmethanol/water/formic acid or acetonitrile/water/formic acid gradientsat a flowrate of 200 μL/min.

Mass spectrometry was carried out by electrospray ionisation in positivemode for the non-polar fraction and negative mode for the polar fractionusing multiple-reactionmonitoring-(MRM)-mode and fullscan from 100-1000amu.

EXAMPLE 2 Data Evaluation

The GC- and LC-MS measurements of all blood plasma samples of risksubjects (subjects with high risk of developing diabetes; either “IFG”:non-diabetes subjects with IFG positive, “IGT”: non-diabetes subjectswith IGT positive, or “IFG&IGT”: non-diabetes subjects with IFG and IGTpositives) and control subjects were conducted together with pooledplasma references. For each measurement batch, relative signal ratios ofsingle subjects were calculated.

Risk-specific metabolites were determined by linear modeling ofmetabolite signal ratios by the ternary factor risk (levels:IFG,IGT,IFG&IGT) correcting for confounding factors age and BMI (bodymass index) and also incorporating the two different genders: first alinear model with the just mentioned factors was generated, secondestimated effects were evaluated by t-statistics, third only metaboliteswere selected with t-statistics p-value <0.05 concerning risk factorlevels and interactions between gender and risk factor levels.Furthermore, regulation type was determined for each metabolite as “up”for increased ratios >1 of the respective risk level vs. control and“down” for decreased ratios <1 of risk level vs. control.

In the following Tables 1 to 5, the results of the data evaluation arepresented. Tables 1 and 2 show the results for metabolites which havenot yet been reported for diabetes patients. The metabolites referred toin Tables 3 to 5 have been described already for diabetes patients.Tables 1 and 3 list metabolites significant with regard to main effect“risk”, specifying the respective significant factor levels IFG, IGT,and IFG&IGT (t-statistics). Tables 2, 4 and 5 list metabolitessignificant with regard to interaction of risk and gender, i.e.,metabolites showing sex-specific differential regulation betweencontrols and risk subjects. The results presented in the tables areranked according to their potential and efficacy as biomarkers fordiabetes or a predisposition thereof. The observed kind of regulation isalso indicated. “Up” refers to an increase in the absolute or relativeamount of the metabolite, while “down” refers to a decrease in saidabsolute or relative amount or even the absence of the metabolite indetectable amounts. Metabolites being particularly strong associatedwith diabetes are subdivided into groups indicated by the dividing linesin the Tables. Moreover, the risk group is indicated in the tables, i.e.IFG, IGT or IFG&IGT.

TABLE 1 Overall results. Metabolites differing significantly (p < 0.05)between risk groups for Diabetes mellitus type 2 (IFG, IGT and IFG&IGT)and controls (significant main effect “risk”, i.e. same regulation type(“up”, “down”) in males and females.). Metabolites sorted by p-value.[IFG = Impaired Fasting Glucose; IGT = Impaired Glucose Tolerance;IFG&IGT: patients having both IFG and IGT] metabolite regulationrisk_group cryptoxanthin down IGT 2-hydroxy-palmitic acid up IFGtriacylglyceride (C16:0, C18:1, C18:2) up IGT gondoic acid up IGTtricosanoic acid down IFG&IGT 5-Oxoproline up IFG

TABLE 2 Metabolites differing specifically between male controls andmale patients from risk groups for Diabetes. Metabolites differingsignificantly (p < 0.05) with regard to interaction risk-gender, i.e.differently regulated in males and females with regard to risk forDiabetes mellitus type 2 (IFG, IGT and IFG&IGT) and controls.Metabolites sorted by p-value. [IFG = Impaired Fasting Glucose; IGT =Impaired Glucose Tolerance; IFG&IGT: patients having both IFG and IGT]metabolite reg_male risk_group diacylglyceride (C18:1, C18:2) down IGTtriacylglyceride (C16:0, C18:2, C18:2) down IGT triacylglyceride (C16:0,C18:1, C18:2) down IFG

TABLE 3 Overall results. Metabolites differing significantly (p < 0.05)between risk groups for Diabetes mellitus type 2 (IFG, IGT and IFG&IGT)and controls (significant main effect “risk”, i.e. same regulation type(“up”, “down”) in males and females.). Metabolites sorted by p-value.[IFG = Impaired Fasting Glucose; IGT = Impaired Glucose Tolerance;IFG&IGT: patients having both IFG and IGT] metabolite regulationrisk_group lactate up IFG alpha-ketoisocaproic acid down IGT glucose upIFG&IGT methionine down IGT mannose up IFG&IGT 3-hydroxybutyric acid upIGT leucine up IGT uric acid up IFG threonic acid up IFG beta-carotenedown IFG&IGT ascorbic acid up IFG&IGT glycine down IGT triacylglyceridesdown IFG lactate up IGT phospholipids up IGT creatinine down IGTglutamate up IFG alpha-ketoisocaproic acid down IFG&IGTtriacylglycerides up IGT valine up IGT malate up IFGalpha-ketoisocaproic acid down IFG isoleucine up IGT succinate up IFGglucose-1-phosphate up IFG&IGT valine up IFG&IGT eicosapentaenoic acid(C20:cis[5, 8, 11, 14, down IFG&IGT 17]5) phospholipids up IFG uric acidup IFG&IGT citrate up IGT aspargine down IFG&IGT methionine down IFGglutamine down IGT palmitic acid up IGT tryptophane down IFG&IGT alanineup IGT glutamate up IGT citrulline down IGT cholestenol down IFG&IGTthreonine down IGT ornithine up IFG arginine down IGT mannose up IFG3-hydroxybutyric acid up IFG&IGT glutamine down IFG pregnenolone sulfateup IFG&IGT glyceric acid up IGT folate up IFG malate up IGTbeta-carotene down IFG leucine up IFG glutamine down IFG&IGTalpha-tocopherol up IFG&IGT myo-inositol up IFG stearic acid up IGTglycerol-3-phosphate up IFG beta-carotene down IGT

TABLE 4 Metabolites differing specifically between male controls andmale patients from risk groups for Diabetes. Metabolites differingsignificantly (p < 0.05) with regard to interaction risk-gender, i.e.differently regulated in males and females with regard to risk forDiabetes mellitus type 2 (IFG, IGT and IFG&IGT) and controls.Metabolites sorted by p-value. [IFG = Impaired Fasting Glucose; IGT =Impaired Glucose Tolerance; IFG&IGT: patients having both IFG and IGT]metabolite reg_male risk_group tryptophane down IGT alanine down IFGleucine down IGT palmitic acid down IFG eicosatrienoic acid down IGTglycerophospholipids down IGT isoleucine down IFG eicosatrienoic aciddown IFG tryptophane down IFG lignoceric acid down IGT linoleic aciddown IGT serine up IFG tyrosine down IGT linoleic acid down IFGpregnenolone sulfate down IGT aspartate up IGT arachidonic acid down IGTsuccinate up IFG&IGT

TABLE 5 Metabolites differing specifically between female controls andfemale patients from risk groups for Diabetes. Metabolites differingsignificantly (p < 0.05) with regard to interaction risk-gender, i.e.differently regulated in males and females with regard to risk forDiabetes mellitus type 2 (IFG, IGT and IFG&IGT) and controls.Metabolites sorted by p-value. [IFG = Impaired Fasting Glucose; IGT =Impaired Glucose Tolerance; IFG&IGT: patients having both IFG and IGT]metabolite reg_female risk_group alanine up IFG palmitic acid up IFGisoleucine up IFG eicosatrienoic acid up IFG uric acid up IFG stearicacid up IFG serine down IFG

1. A method for diagnosing a predisposition for diabetes comprising: (a)determining at least one metabolite in a test sample of a subjectsuspected to have a predisposition for diabetes, said at least onemetabolite selected from the group consisting of gondoic acid andtricosanoic acid to obtain test results; and (b) comparing the testresults of the determination in step (a) to a reference, whereby apredisposition for diabetes is to be diagnosed.
 2. The method of claim1, wherein at least one additional metabolite is determined and isselected from the group consisting of diacylglyceride (C18:1,C18:2) andtriacylglyceride (C16:0,C18:2,C18:2).
 3. The method of claim 2, whereinsaid subject is a male.
 4. The method of claim 1, wherein saiddetermining the said at least one metabolite comprises mass spectrometry(MS).
 5. The method of claim 4, wherein said mass spectrometry is liquidchromatography (LC) MS and/or gas chromatography (GC) MS.
 6. The methodof claim 5, wherein the metabolite is gondoic acid.
 7. The method ofclaim 5, wherein the metabolite is tricosanoic acid.
 8. The method ofclaim 1, wherein said reference is derived from a subject known to havea predisposition for diabetes.
 9. The method of claim 8, whereinidentical or similar results for the test sample and the reference areindicative for a predisposition for diabetes.
 10. The method of claim 1,wherein said reference is derived from a subject known to have nopredisposition for diabetes.
 11. The method of claim 10, wherein theabsence of the said at least one metabolite or an amount thereof whichdiffers in the test sample in comparison to the reference sample isindicative for a predisposition for diabetes.
 12. The method of claim 1,wherein said reference is a calculated reference for the said at leastone metabolite in a population of subjects.
 13. The method of claim 12,wherein the absence of the said at least one metabolite or an amountthereof which differs in the test sample in comparison to the referencesample is indicative for a predisposition for diabetes.
 14. The methodof claim 1, wherein said sample is a sample of a body fluid of saidsubject.
 15. The method of claim 1, wherein said subject is a human. 16.The method of claim 1, wherein at least one additional metabolite isdetermined selected from the group consisting of: (i) a long-chainsaturated fatty acid, (ii) a poly-unsaturated fatty acid, (iii) an aminoacid, (iv) an antioxidant, (v) a metabolite of the Citric Acid Cycle,(vi) a metabolite of the Urea Cycle, (vii) Mannose, alpha-Ketoisocaproicacid, Glycerol, lipid fraction, or 3-Hydroxybutyric acid, and (viii)glucose.
 17. The method of claim 16, wherein the long chain saturatedfatty acid is selected from the group consisting of Lignoceric acid(C24:0) and Melissic acid (C30:0).
 18. The method of claim 16, whereinthe poly-unsaturated fatty acid is selected from the group consisting ofDocosahexaenoic acid (C22:eis[4,7,10,13,16,19]6), Eicosapentaenoic acid(C20:cis[5,8,11,14,17]5), Arachidonic acid (C20:cis-[5,8,11,14]4),Linoleic acid (C18:cis[9,12]2), and Linolenic acid (C18:cis[9,12,15]3).19. The method of claim 16, wherein the amino acid is selected from thegroup consisting of Lysine, Alanine, Threonine, Tryptophane, Valine,Isoleucine, Leucine, Cysteine, Methionine, Tyrosine, Phenylalanine,Glycine, Proline, and Glutamine.
 20. The method of claim 16, wherein theantioxidant is selected from the group consisting of Ascorbic acid,Coenzyme Q10, and alpha-Tocopherol.
 21. The method of claim 16, whereinthe metabolite of the Citric Acid Cycle is selected from the groupconsisting of Pyruvate, Citrate, and Malate.
 22. The method of claim 16,wherein the metabolite of the Urea cycle is selected from the groupconsisting of Urea, Citrulline, Succinate, and Ornithine.
 23. The methodof claim 1, wherein at least one additional metabolite is determinedselected from the group consisting of: 3-hydroxybutyric acid, alanine,alpha-ketoisocaproic acid, alpha-tocopherol, arginine, ascorbic acid,aspargine, beta-carotene, cholestenol, citrate, citrulline, creatinine,eicosapentaenoic acid (C20:cis[5,8,11,14,17]5), folate, glucose,glucose-1-phosphate, glutamate, glutamine, glyceric acid,glycerol-3-phosphate, glycine, isoleucine, lactate, leucine, malate,mannose, methionine, myo-inositol, ornithine, palmitic acid,phospholipids, pregnenolone sulfate, stearic acid, succinate, threonicacid, threonine, triacylglycerides, tryptophane, uric acid, and valine.